A sequence that keeps AI work accountable: one workflow, one baseline, one team, and one honest decision at the end of the quarter.
Name the task, its current owner, the volume it runs at, and the decision its output feeds. Reject any candidate where those four facts cannot be written in a sentence each.
Capture cycle time, error rate, rework rate, and cost per unit as they stand today. This is the only number the project will later be judged against, so it has to be collected before anything changes.
Document which decisions stay human, which data never leaves your systems, and which output always requires sign-off. Approvals move faster when the exclusions are explicit.
Ship the smallest thing that touches real data for real users, with logging and monitoring in place from the first day rather than added after launch.
Give a single team the new workflow as their default for several weeks while the old process stays available. Watch how often they fall back, and why.
Compare against the baseline and make an explicit call: widen it, fix a specific gap, or stop. A project without a stop option tends to keep consuming budget indefinitely.
We will walk your first workflow through these phases and tell you where the plan is likely to stall before you spend the budget.